Social robots powered by generative AI such as Large Language Models open new possibilities for human-robot interaction by enabling natural, human-like conversations. Consequently, these generative social robots (GSRs) become more capable of influencing user attitudes and behavior through persuasion. In the present research, we conducted qualitative interviews with caregivers and therapists to identify knowledge-based design requirements for persuasive GSRs that promote physiotherapy attendance of residents in a Swiss eldercare facility. Our findings demonstrate that available information about the robot’s role as a caregiver assistant, as well as its assertive and polite personality would increase the robot’s ability to build trust and acceptance. Furthermore, having information on context factors like therapy benefits, facility routines, and news updates would allow the robot to flexibly and adequately adapt its persuasion attempts. Lastly, available information regarding user biography, emotions, and health status, enables the robot to engage in personalized persuasion. Taken together, the present findings highlight the importance of considering self-, context-, and user-related knowledge as key dimensions to integrate into GSRs. This could be realized through prompting techniques, fine-tuning, database access, and the robot’s real-time perception.